Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine

被引：0

作者：

Park, Hyeong-jun ^{[1
]}

Kim, Jongin ^{[1
]}

Min, Beomjun ^{[1
]}

Lee, Boreom ^{[1
]}

机构：

[1] Gwangju Inst Sci & Technol, Inst Integrated Technol, Dept Biomed Sci & Engn BMSE, Gwangju, South Korea

来源：

2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2017年

基金：

新加坡国家研究基金会;

关键词：

motor imagery based brain-computer interfaces (MI BCIs); wavelet packet decomposition common spatial pattern (WPD-CSP); feature selection; extreme learning machine (ELM); BRAIN-COMPUTER INTERFACES; FEATURE-EXTRACTION; CSP; BCI; SYSTEM;

D O I：

暂无

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

Performance of motor imagery based brain-computer interfaces (MI BCIs) greatly depends on how to extract the features. Various versions of filter-bank based common spatial pattern have been proposed and used in MI BCIs. Filter-bank based common spatial pattern has more number of features compared with original common spatial pattern. As the number of features increases, the MI BCIs using filter-bank based common spatial pattern can face overfitting problems. In this study, we used eigenvector centrality feature selection method, wavelet packet decomposition common spatial pattern, and kernel extreme learning machine to improve the performance of MI BCIs and avoid overfitting problems. Furthermore, the computational speed was improved by using kernel extreme learning machine.

引用

页码：2863 / 2866

页数：4

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